SambaNova Public Sector的动态

Are you excited about the potential of vector search but concerned about the high costs associated with production-level deployment? Quantization and adaptive retrieval can help you optimize your vector database usage for performance and affordability. Thanks to Zain Hasan from Weaviate for sharing: Quantization techniques: Discover how to compress vectors to reduce memory requirements while maintaining accuracy. Adaptive retrieval: Understand how to balance latency and accuracy by performing efficient, low-accuracy searches followed by more precise restoring. Practical applications: Learn how to achieve up to 32x reduction in memory costs without sacrificing significant retrieval performance. MLOps World Generative AI World #vectorsearch #LLMs?

?? Read our latest blog from Bram-Ernst Verhoef and Martino Dazzi about how our quantization methods make the Metis AIPU highly efficient and accurate. To create a high-performing and highly energy-efficient AI processing unit (AIPU) that obsoletes extensive model retraining, our engineers took a radically different approach to data processing. Through unique quantization methods and a proprietary system architecture, Axelera AI can offer the most powerful AI accelerator for the edge you can buy today. In this blog, you can read all about our unique quantization techniques. ? https://lnkd.in/d7xPeDTj ? #AI #MachineLearning #EdgeComputing #AIAccelerator #Quantization #AIPU #TechInnovation #DeepLearning #ArtificialIntelligence #EnergyEfficiency #AxeleraAI

Foursquare Brings Enterprise-Grade Spatial Analytics to Your Browser with Kepler.gl 3.1??? Kepler.gl 3.1 delivers cutting-edge spatial analysis directly in your browser, powered by cloud-native data formats, generative AI, and edge computing. This release revolutionizes how teams visualize and analyze location data—no heavy infrastructure needed. Learn more about this game-changing update. Read the blog from Vikram Gundeti:???? https://hubs.la/Q034WKX_0 #KeplerGL #OpenJS #Geospatial

AI architecture advances rapidly as capabilities grow. Take the upcoming agentic AI as an example. Learn more about AI architectures in this lively webinar with industry leaders - Vladimir Stojanovic from Ayar Labs, Nidhi Chappell (She/Her)from Microsoft, and Jean-Philippe Fricker from Cerebras Systems join moderator Timothy Prickett Morgan from The Next Platform for a discussion on Unlocking the Future of AI Infrastructure. Register for the webinar, held on October 24th, at https://lnkd.in/gGTMTcFu #ai #hpc #siliconphotonics

?? Excited to Share My Latest Technical Article: Integrating Claude MCP Concepts with Agentic AI in AI-RAN Environments ?? In the rapidly evolving landscape of AI-enabled Radio Access Networks (AI-RAN), the intersection of Model Context Protocol (MCP) and Agentic AI opens a new frontier for innovation. My latest technical article delves into this exciting space, presenting a framework for enhancing contextual understanding, distributed decision-making, and multi-agent coordination in telecom networks. ?? Highlights from the Article: MCP in AI-RAN: Explore how MCP principles transform network state modeling, action mapping, and multi-level context hierarchies. Stochastic Game Theory: Uncover the role of probabilistic models and dynamic equilibria in optimizing uncertain network conditions. Distributed AI Architecture: Dive into practical implementation strategies, including multi-agent systems and context-aware resource allocation. Performance Gains: Discover how MCP can address key challenges like the curse of dimensionality and multi-agent coordination. ?? Key Takeaways: Context-aware resource allocation strategies that dynamically adapt to network conditions Hierarchical architectures for scalable deployments Practical code examples demonstrating these concepts in action ?? This work also outlines future research directions for developing advanced context models, semantic understanding, and scalable MCP architectures tailored to next-generation networks. ?? Whether you're working on AI in telecom or exploring multi-agent systems, I hope this article provides valuable insights and sparks further discussions in our community. #AI #RAN #AIRAN #AgenticAI #ModelContextProtocol #TelecomInnovation #AIinTelecom #FutureNetworks #ClaudeMCP

?? Exciting developments in #AI and #GraphTechnology! Discover how GraphRAG Agent with Neo4j & Milvus revolutionizes data handling! From enhanced scalability to improved insights, this integration is set to transform your projects. "This approach merges Neo4j and Milvus to enhance traditional Retrieval Augmented Generation systems, offering more accurate and nuanced answers to complex queries." Dive into the full story on Neo4j's developer blog now: [Read more] https://lnkd.in/eNz3C_Zu ???? #TechInnovation #DataScience #Neo4j #Milvus

Revolutionizing Foundation Models with MiniMax-01! We're excited to share MiniMax-01, a breakthrough in AI scalability and efficiency. Here's what makes it exceptional: Model Overview: Total Parameters: 456 billion Activated Parameters per Token: 45.9 billion Training Context Length: 1 million tokens Inference Context Length: Up to 4 million tokens Hybrid Architecture: Combines Lightning Attention, Softmax Attention, and Mixture-of-Experts (MoE) for unmatched performance. Advanced Parallel Strategies: Linear Attention Sequence Parallelism Plus (LASP+) Varlen Ring Attention Expert Tensor Parallel (ETP) With context windows reaching up to 4M tokens and innovative optimizations, MiniMax-01 rivals the performance of GPT-4o and Claude-3.5-Sonnet while delivering unparalleled capabilities for long-context tasks. Learn more and explore the open-source release: https://lnkd.in/dYat4bAN Read the full paper:https://lnkd.in/d72RFide #AI #FoundationModels #MachineLearning #Innovation #TechBreakthrough

Generative AI (GenAI) applications are inherently insecure because of the vector database backend used in typical RAG (Retrieval Augmented Generation) applications.?Until yesterday,?there was no way to secure a vector database. Super happy ?? to announce beta launch of VectorX (https://vectorxdb.ai) world's most secure vector database. VectorX uses homomorphic ANN to perform fast ANN queries inside the encryption leaving zero network footprint for the vectors or metadata, thus ensuring complete privacy and eliminating the risk of data exposure. VectorX is fast, available in multiple zones and on-prem and beats leading vector databases in precision and recall. Please fill the form or DM for a demo and early access. #GenAI #VectorDB #VectorX

Looking forward to participating in this discussion on the impact of simulated data on AI and data centric frameworks. #AI #DataScience #Analytics